Overview

Dataset statistics

Number of variables19
Number of observations1940
Missing cells5441
Missing cells (%)14.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory288.1 KiB
Average record size in memory152.1 B

Variable types

Text6
Categorical5
Numeric6
DateTime2

Alerts

State has constant value ""Constant
EV Level1 EVSE Num is highly overall correlated with EV Level2 EVSE Num and 1 other fieldsHigh correlation
EV Level2 EVSE Num is highly overall correlated with EV Level1 EVSE NumHigh correlation
Groups With Access Code is highly overall correlated with Date Last ConfirmedHigh correlation
EV Network is highly overall correlated with EV Level1 EVSE Num and 1 other fieldsHigh correlation
Date Last Confirmed is highly overall correlated with Groups With Access Code and 1 other fieldsHigh correlation
Groups With Access Code is highly imbalanced (90.5%)Imbalance
Date Last Confirmed is highly imbalanced (64.9%)Imbalance
EV Connector Types is highly imbalanced (67.5%)Imbalance
Access Days Time has 162 (8.4%) missing valuesMissing
EV Level1 EVSE Num has 1932 (99.6%) missing valuesMissing
EV Level2 EVSE Num has 205 (10.6%) missing valuesMissing
EV DC Fast Count has 1679 (86.5%) missing valuesMissing
EV Pricing has 1460 (75.3%) missing valuesMissing
ID has unique valuesUnique

Reproduction

Analysis started2023-10-12 07:41:28.834678
Analysis finished2023-10-12 07:41:32.951601
Duration4.12 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct1928
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2023-10-12T00:41:33.265171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length72
Median length55
Mean length23.430412
Min length3

Characters and Unicode

Total characters45455
Distinct characters80
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1918 ?
Unique (%)98.9%

Sample

1st rowCity of Lacey - City Hall Parking
2nd rowSeattle-Tacoma International Airport - General Parking
3rd rowAvista Corp
4th rowSteam Plant Parking
5th rowBELLEVUE BELLEVUE CH 1
ValueCountFrequency (%)
477
 
5.9%
1 199
 
2.5%
amz 185
 
2.3%
tesla 161
 
2.0%
station 153
 
1.9%
2 142
 
1.8%
destination 113
 
1.4%
wa 79
 
1.0%
garage 78
 
1.0%
seattle 72
 
0.9%
Other values (1999) 6426
79.5%
2023-10-12T00:41:33.694416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6151
 
13.5%
e 2368
 
5.2%
a 1882
 
4.1%
A 1856
 
4.1%
t 1541
 
3.4%
S 1511
 
3.3%
E 1484
 
3.3%
n 1447
 
3.2%
r 1362
 
3.0%
T 1330
 
2.9%
Other values (70) 24523
54.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17762
39.1%
Lowercase Letter 17032
37.5%
Space Separator 6151
 
13.5%
Decimal Number 3324
 
7.3%
Dash Punctuation 603
 
1.3%
Other Punctuation 493
 
1.1%
Close Punctuation 39
 
0.1%
Open Punctuation 39
 
0.1%
Math Symbol 6
 
< 0.1%
Connector Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2368
13.9%
a 1882
11.0%
t 1541
9.0%
n 1447
8.5%
r 1362
8.0%
o 1236
 
7.3%
i 1209
 
7.1%
l 1180
 
6.9%
s 980
 
5.8%
u 474
 
2.8%
Other values (17) 3353
19.7%
Uppercase Letter
ValueCountFrequency (%)
A 1856
 
10.4%
S 1511
 
8.5%
E 1484
 
8.4%
T 1330
 
7.5%
C 1137
 
6.4%
L 1019
 
5.7%
I 968
 
5.4%
N 926
 
5.2%
O 886
 
5.0%
R 854
 
4.8%
Other values (16) 5791
32.6%
Decimal Number
ValueCountFrequency (%)
1 835
25.1%
2 614
18.5%
0 392
11.8%
4 335
10.1%
3 317
 
9.5%
5 246
 
7.4%
6 195
 
5.9%
9 154
 
4.6%
7 125
 
3.8%
8 111
 
3.3%
Other Punctuation
ValueCountFrequency (%)
# 214
43.4%
, 98
19.9%
& 61
 
12.4%
. 47
 
9.5%
' 35
 
7.1%
: 25
 
5.1%
/ 10
 
2.0%
\ 2
 
0.4%
@ 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
| 4
66.7%
+ 2
33.3%
Space Separator
ValueCountFrequency (%)
6151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 603
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34794
76.5%
Common 10661
 
23.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2368
 
6.8%
a 1882
 
5.4%
A 1856
 
5.3%
t 1541
 
4.4%
S 1511
 
4.3%
E 1484
 
4.3%
n 1447
 
4.2%
r 1362
 
3.9%
T 1330
 
3.8%
o 1236
 
3.6%
Other values (43) 18777
54.0%
Common
ValueCountFrequency (%)
6151
57.7%
1 835
 
7.8%
2 614
 
5.8%
- 603
 
5.7%
0 392
 
3.7%
4 335
 
3.1%
3 317
 
3.0%
5 246
 
2.3%
# 214
 
2.0%
6 195
 
1.8%
Other values (17) 759
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45453
> 99.9%
None 1
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6151
 
13.5%
e 2368
 
5.2%
a 1882
 
4.1%
A 1856
 
4.1%
t 1541
 
3.4%
S 1511
 
3.3%
E 1484
 
3.3%
n 1447
 
3.2%
r 1362
 
3.0%
T 1330
 
2.9%
Other values (68) 24521
53.9%
None
ValueCountFrequency (%)
é 1
100.0%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%
Distinct1326
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2023-10-12T00:41:33.953561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length45
Median length35
Mean length17.373196
Min length7

Characters and Unicode

Total characters33704
Distinct characters69
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1088 ?
Unique (%)56.1%

Sample

1st row420 College St
2nd row17801 Pacific Hwy S
3rd row1411 E Mission Ave
4th row130 S Post St
5th row450 110th Ave NE
ValueCountFrequency (%)
ave 624
 
8.5%
ne 393
 
5.4%
st 378
 
5.2%
way 192
 
2.6%
n 153
 
2.1%
s 150
 
2.1%
se 124
 
1.7%
rd 120
 
1.6%
street 100
 
1.4%
avenue 97
 
1.3%
Other values (1718) 4984
68.1%
2023-10-12T00:41:34.339873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5383
 
16.0%
e 2148
 
6.4%
1 2017
 
6.0%
t 1956
 
5.8%
0 1622
 
4.8%
a 1194
 
3.5%
2 1085
 
3.2%
r 1072
 
3.2%
S 1022
 
3.0%
v 935
 
2.8%
Other values (59) 15270
45.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13997
41.5%
Decimal Number 8678
25.7%
Uppercase Letter 5447
 
16.2%
Space Separator 5383
 
16.0%
Other Punctuation 159
 
0.5%
Dash Punctuation 37
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2148
15.3%
t 1956
14.0%
a 1194
8.5%
r 1072
 
7.7%
v 935
 
6.7%
h 840
 
6.0%
n 792
 
5.7%
o 783
 
5.6%
i 711
 
5.1%
l 695
 
5.0%
Other values (15) 2871
20.5%
Uppercase Letter
ValueCountFrequency (%)
S 1022
18.8%
A 814
14.9%
N 698
12.8%
E 674
12.4%
W 509
9.3%
B 230
 
4.2%
R 205
 
3.8%
C 176
 
3.2%
D 165
 
3.0%
P 160
 
2.9%
Other values (14) 794
14.6%
Decimal Number
ValueCountFrequency (%)
1 2017
23.2%
0 1622
18.7%
2 1085
12.5%
5 883
10.2%
3 737
 
8.5%
8 582
 
6.7%
4 538
 
6.2%
6 405
 
4.7%
7 405
 
4.7%
9 404
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 132
83.0%
, 17
 
10.7%
& 7
 
4.4%
# 2
 
1.3%
/ 1
 
0.6%
Space Separator
ValueCountFrequency (%)
5383
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19444
57.7%
Common 14260
42.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2148
 
11.0%
t 1956
 
10.1%
a 1194
 
6.1%
r 1072
 
5.5%
S 1022
 
5.3%
v 935
 
4.8%
h 840
 
4.3%
A 814
 
4.2%
n 792
 
4.1%
o 783
 
4.0%
Other values (39) 7888
40.6%
Common
ValueCountFrequency (%)
5383
37.7%
1 2017
 
14.1%
0 1622
 
11.4%
2 1085
 
7.6%
5 883
 
6.2%
3 737
 
5.2%
8 582
 
4.1%
4 538
 
3.8%
6 405
 
2.8%
7 405
 
2.8%
Other values (10) 603
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5383
 
16.0%
e 2148
 
6.4%
1 2017
 
6.0%
t 1956
 
5.8%
0 1622
 
4.8%
a 1194
 
3.5%
2 1085
 
3.2%
r 1072
 
3.2%
S 1022
 
3.0%
v 935
 
2.8%
Other values (59) 15270
45.3%

City
Text

Distinct210
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2023-10-12T00:41:34.653333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length17
Mean length7.8804124
Min length2

Characters and Unicode

Total characters15288
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)4.1%

Sample

1st rowLacey
2nd rowSeattle
3rd rowSpokane
4th rowSpokane
5th rowBellevue
ValueCountFrequency (%)
seattle 468
21.9%
bellevue 261
 
12.2%
tacoma 88
 
4.1%
spokane 66
 
3.1%
vancouver 45
 
2.1%
walla 42
 
2.0%
renton 39
 
1.8%
olympia 38
 
1.8%
bellingham 35
 
1.6%
kirkland 34
 
1.6%
Other values (223) 1018
47.7%
2023-10-12T00:41:35.051381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2654
17.4%
l 1679
 
11.0%
a 1541
 
10.1%
t 1292
 
8.5%
n 777
 
5.1%
o 725
 
4.7%
S 624
 
4.1%
u 564
 
3.7%
r 458
 
3.0%
i 444
 
2.9%
Other values (43) 4530
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12902
84.4%
Uppercase Letter 2181
 
14.3%
Space Separator 194
 
1.3%
Other Punctuation 7
 
< 0.1%
Decimal Number 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2654
20.6%
l 1679
13.0%
a 1541
11.9%
t 1292
10.0%
n 777
 
6.0%
o 725
 
5.6%
u 564
 
4.4%
r 458
 
3.5%
i 444
 
3.4%
v 391
 
3.0%
Other values (14) 2377
18.4%
Uppercase Letter
ValueCountFrequency (%)
S 624
28.6%
B 387
17.7%
T 139
 
6.4%
L 110
 
5.0%
R 107
 
4.9%
W 105
 
4.8%
P 88
 
4.0%
V 70
 
3.2%
A 70
 
3.2%
E 69
 
3.2%
Other values (14) 412
18.9%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
8 1
25.0%
9 1
25.0%
Space Separator
ValueCountFrequency (%)
194
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15083
98.7%
Common 205
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2654
17.6%
l 1679
 
11.1%
a 1541
 
10.2%
t 1292
 
8.6%
n 777
 
5.2%
o 725
 
4.8%
S 624
 
4.1%
u 564
 
3.7%
r 458
 
3.0%
i 444
 
2.9%
Other values (38) 4325
28.7%
Common
ValueCountFrequency (%)
194
94.6%
. 7
 
3.4%
0 2
 
1.0%
8 1
 
0.5%
9 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2654
17.4%
l 1679
 
11.0%
a 1541
 
10.1%
t 1292
 
8.5%
n 777
 
5.1%
o 725
 
4.7%
S 624
 
4.1%
u 564
 
3.7%
r 458
 
3.0%
i 444
 
2.9%
Other values (43) 4530
29.6%

State
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
WA
1940 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3880
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 1940
100.0%

Length

2023-10-12T00:41:35.154729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:41:35.229338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
wa 1940
100.0%

Most occurring characters

ValueCountFrequency (%)
W 1940
50.0%
A 1940
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3880
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 1940
50.0%
A 1940
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3880
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 1940
50.0%
A 1940
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 1940
50.0%
A 1940
50.0%

ZIP
Text

Distinct303
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2023-10-12T00:41:35.537424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters9700
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)4.6%

Sample

1st row98503
2nd row98188
3rd row99252
4th row99201
5th row98004
ValueCountFrequency (%)
98004 157
 
8.1%
98121 107
 
5.5%
98109 98
 
5.0%
98005 67
 
3.5%
98101 43
 
2.2%
98104 36
 
1.9%
98119 35
 
1.8%
98188 30
 
1.5%
98052 27
 
1.4%
99224 26
 
1.3%
Other values (294) 1315
67.7%
2023-10-12T00:41:35.959496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 2391
24.6%
8 2024
20.9%
0 1392
14.4%
1 1061
10.9%
2 842
 
8.7%
4 513
 
5.3%
3 474
 
4.9%
5 412
 
4.2%
6 365
 
3.8%
7 223
 
2.3%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9697
> 99.9%
Uppercase Letter 2
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 2391
24.7%
8 2024
20.9%
0 1392
14.4%
1 1061
10.9%
2 842
 
8.7%
4 513
 
5.3%
3 474
 
4.9%
5 412
 
4.2%
6 365
 
3.8%
7 223
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
G 1
50.0%
N 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9698
> 99.9%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
9 2391
24.7%
8 2024
20.9%
0 1392
14.4%
1 1061
10.9%
2 842
 
8.7%
4 513
 
5.3%
3 474
 
4.9%
5 412
 
4.2%
6 365
 
3.8%
7 223
 
2.3%
Latin
ValueCountFrequency (%)
G 1
50.0%
N 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 2391
24.6%
8 2024
20.9%
0 1392
14.4%
1 1061
10.9%
2 842
 
8.7%
4 513
 
5.3%
3 474
 
4.9%
5 412
 
4.2%
6 365
 
3.8%
7 223
 
2.3%
Other values (3) 3
 
< 0.1%

Groups With Access Code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
Public
1885 
Public - Call ahead
 
29
Public - Credit card at all times
 
24
Public - Card key after hours
 
1
Public - Card key at all times
 
1

Length

Max length33
Median length6
Mean length6.5525773
Min length6

Characters and Unicode

Total characters12712
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowPublic
2nd rowPublic
3rd rowPublic
4th rowPublic
5th rowPublic

Common Values

ValueCountFrequency (%)
Public 1885
97.2%
Public - Call ahead 29
 
1.5%
Public - Credit card at all times 24
 
1.2%
Public - Card key after hours 1
 
0.1%
Public - Card key at all times 1
 
0.1%

Length

2023-10-12T00:41:36.072032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:41:36.157739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
public 1940
88.9%
55
 
2.5%
call 29
 
1.3%
ahead 29
 
1.3%
card 26
 
1.2%
at 25
 
1.1%
all 25
 
1.1%
times 25
 
1.1%
credit 24
 
1.1%
key 2
 
0.1%
Other values (2) 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l 2048
16.1%
i 1989
15.6%
c 1964
15.4%
u 1941
15.3%
P 1940
15.3%
b 1940
15.3%
242
 
1.9%
a 164
 
1.3%
e 81
 
0.6%
d 79
 
0.6%
Other values (11) 324
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10420
82.0%
Uppercase Letter 1995
 
15.7%
Space Separator 242
 
1.9%
Dash Punctuation 55
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 2048
19.7%
i 1989
19.1%
c 1964
18.8%
u 1941
18.6%
b 1940
18.6%
a 164
 
1.6%
e 81
 
0.8%
d 79
 
0.8%
t 75
 
0.7%
r 52
 
0.5%
Other values (7) 87
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
P 1940
97.2%
C 55
 
2.8%
Space Separator
ValueCountFrequency (%)
242
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12415
97.7%
Common 297
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 2048
16.5%
i 1989
16.0%
c 1964
15.8%
u 1941
15.6%
P 1940
15.6%
b 1940
15.6%
a 164
 
1.3%
e 81
 
0.7%
d 79
 
0.6%
t 75
 
0.6%
Other values (9) 194
 
1.6%
Common
ValueCountFrequency (%)
242
81.5%
- 55
 
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 2048
16.1%
i 1989
15.6%
c 1964
15.4%
u 1941
15.3%
P 1940
15.3%
b 1940
15.3%
242
 
1.9%
a 164
 
1.3%
e 81
 
0.6%
d 79
 
0.6%
Other values (11) 324
 
2.5%

Access Days Time
Text

MISSING 

Distinct61
Distinct (%)3.4%
Missing162
Missing (%)8.4%
Memory size15.3 KiB
2023-10-12T00:41:36.361223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length159
Median length14
Mean length24.456693
Min length11

Characters and Unicode

Total characters43484
Distinct characters56
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)2.2%

Sample

1st row24 hours daily
2nd row24 hours daily; pay lot; Drivers must bring their own J1772 cordset for Level 1 charging
3rd row24 hours daily; Drivers must bring their own J1772 cordset for Level 1 charging
4th row24 hours daily
5th row24 hours daily
ValueCountFrequency (%)
hours 1643
19.1%
daily 1626
18.9%
24 1612
18.7%
817
9.5%
10:59pm 658
7.6%
5:00am 644
 
7.5%
sun 120
 
1.4%
sat 120
 
1.4%
fri 117
 
1.4%
thu 117
 
1.4%
Other values (110) 1149
13.3%
2023-10-12T00:41:36.676805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6845
 
15.7%
a 2706
 
6.2%
0 2531
 
5.8%
u 2115
 
4.9%
r 1946
 
4.5%
o 1912
 
4.4%
i 1903
 
4.4%
s 1885
 
4.3%
h 1812
 
4.2%
d 1777
 
4.1%
Other values (46) 18052
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23436
53.9%
Decimal Number 8998
 
20.7%
Space Separator 6845
 
15.7%
Other Punctuation 2424
 
5.6%
Uppercase Letter 921
 
2.1%
Dash Punctuation 852
 
2.0%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2706
11.5%
u 2115
9.0%
r 1946
8.3%
o 1912
8.2%
i 1903
8.1%
s 1885
8.0%
h 1812
7.7%
d 1777
7.6%
m 1741
7.4%
l 1731
7.4%
Other values (13) 3908
16.7%
Uppercase Letter
ValueCountFrequency (%)
S 243
26.4%
T 237
25.7%
F 123
13.4%
M 123
13.4%
W 123
13.4%
D 31
 
3.4%
J 9
 
1.0%
L 8
 
0.9%
U 6
 
0.7%
C 6
 
0.7%
Other values (6) 12
 
1.3%
Decimal Number
ValueCountFrequency (%)
0 2531
28.1%
2 1679
18.7%
4 1641
18.2%
5 1360
15.1%
1 838
 
9.3%
9 752
 
8.4%
7 133
 
1.5%
6 34
 
0.4%
8 30
 
0.3%
Other Punctuation
ValueCountFrequency (%)
: 1634
67.4%
; 780
32.2%
, 9
 
0.4%
/ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6845
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 852
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24357
56.0%
Common 19127
44.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2706
11.1%
u 2115
8.7%
r 1946
8.0%
o 1912
 
7.8%
i 1903
 
7.8%
s 1885
 
7.7%
h 1812
 
7.4%
d 1777
 
7.3%
m 1741
 
7.1%
l 1731
 
7.1%
Other values (29) 4829
19.8%
Common
ValueCountFrequency (%)
6845
35.8%
0 2531
 
13.2%
2 1679
 
8.8%
4 1641
 
8.6%
: 1634
 
8.5%
5 1360
 
7.1%
- 852
 
4.5%
1 838
 
4.4%
; 780
 
4.1%
9 752
 
3.9%
Other values (7) 215
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6845
 
15.7%
a 2706
 
6.2%
0 2531
 
5.8%
u 2115
 
4.9%
r 1946
 
4.5%
o 1912
 
4.4%
i 1903
 
4.4%
s 1885
 
4.3%
h 1812
 
4.2%
d 1777
 
4.1%
Other values (46) 18052
41.5%

EV Level1 EVSE Num
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)75.0%
Missing1932
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean22.875
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-10-12T00:41:36.762612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.35
Q12
median3
Q317.75
95-th percentile93
Maximum121
Range120
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation41.858392
Coefficient of variation (CV)1.8298751
Kurtosis5.5723383
Mean22.875
Median Absolute Deviation (MAD)1.5
Skewness2.3481563
Sum183
Variance1752.125
MonotonicityNot monotonic
2023-10-12T00:41:36.829982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 3
 
0.2%
121 1
 
0.1%
1 1
 
0.1%
10 1
 
0.1%
4 1
 
0.1%
41 1
 
0.1%
(Missing) 1932
99.6%
ValueCountFrequency (%)
1 1
 
0.1%
2 3
0.2%
4 1
 
0.1%
10 1
 
0.1%
41 1
 
0.1%
121 1
 
0.1%
ValueCountFrequency (%)
121 1
 
0.1%
41 1
 
0.1%
10 1
 
0.1%
4 1
 
0.1%
2 3
0.2%
1 1
 
0.1%

EV Level2 EVSE Num
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)1.0%
Missing205
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean2.2910663
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-10-12T00:41:36.900047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile6
Maximum44
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0458994
Coefficient of variation (CV)0.89299006
Kurtosis118.53345
Mean2.2910663
Median Absolute Deviation (MAD)0
Skewness8.0520612
Sum3975
Variance4.1857045
MonotonicityNot monotonic
2023-10-12T00:41:36.967705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 1085
55.9%
1 395
 
20.4%
4 83
 
4.3%
3 63
 
3.2%
6 43
 
2.2%
8 18
 
0.9%
5 14
 
0.7%
10 10
 
0.5%
7 7
 
0.4%
14 4
 
0.2%
Other values (7) 13
 
0.7%
(Missing) 205
 
10.6%
ValueCountFrequency (%)
1 395
 
20.4%
2 1085
55.9%
3 63
 
3.2%
4 83
 
4.3%
5 14
 
0.7%
6 43
 
2.2%
7 7
 
0.4%
8 18
 
0.9%
9 3
 
0.2%
10 10
 
0.5%
ValueCountFrequency (%)
44 1
 
0.1%
24 1
 
0.1%
20 2
 
0.1%
15 2
 
0.1%
14 4
 
0.2%
12 3
 
0.2%
11 1
 
0.1%
10 10
0.5%
9 3
 
0.2%
8 18
0.9%

EV DC Fast Count
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)4.6%
Missing1679
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean3.6934866
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-10-12T00:41:37.033209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile12
Maximum20
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.8508136
Coefficient of variation (CV)1.0425958
Kurtosis2.5540044
Mean3.6934866
Median Absolute Deviation (MAD)1
Skewness1.749871
Sum964
Variance14.828765
MonotonicityNot monotonic
2023-10-12T00:41:37.100925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 113
 
5.8%
4 37
 
1.9%
2 33
 
1.7%
8 20
 
1.0%
3 20
 
1.0%
12 15
 
0.8%
6 7
 
0.4%
16 6
 
0.3%
10 5
 
0.3%
5 3
 
0.2%
Other values (2) 2
 
0.1%
(Missing) 1679
86.5%
ValueCountFrequency (%)
1 113
5.8%
2 33
 
1.7%
3 20
 
1.0%
4 37
 
1.9%
5 3
 
0.2%
6 7
 
0.4%
8 20
 
1.0%
10 5
 
0.3%
12 15
 
0.8%
14 1
 
0.1%
ValueCountFrequency (%)
20 1
 
0.1%
16 6
 
0.3%
14 1
 
0.1%
12 15
0.8%
10 5
 
0.3%
8 20
1.0%
6 7
 
0.4%
5 3
 
0.2%
4 37
1.9%
3 20
1.0%

EV Network
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
ChargePoint Network
973 
Blink Network
302 
Non-Networked
162 
Tesla Destination
112 
Volta
106 
Other values (11)
285 

Length

Max length19
Median length19
Mean length15.42268
Min length3

Characters and Unicode

Total characters29920
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowNon-Networked
2nd rowNon-Networked
3rd rowNon-Networked
4th rowNon-Networked
5th rowChargePoint Network

Common Values

ValueCountFrequency (%)
ChargePoint Network 973
50.2%
Blink Network 302
 
15.6%
Non-Networked 162
 
8.4%
Tesla Destination 112
 
5.8%
Volta 106
 
5.5%
SHELL_RECHARGE 58
 
3.0%
Tesla 49
 
2.5%
Electrify America 49
 
2.5%
EV Connect 36
 
1.9%
eVgo Network 28
 
1.4%
Other values (6) 65
 
3.4%

Length

2023-10-12T00:41:37.184219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
network 1303
37.9%
chargepoint 973
28.3%
blink 302
 
8.8%
non-networked 162
 
4.7%
tesla 161
 
4.7%
destination 112
 
3.3%
volta 106
 
3.1%
shell_recharge 58
 
1.7%
america 49
 
1.4%
electrify 49
 
1.4%
Other values (9) 165
 
4.8%

Most occurring characters

ValueCountFrequency (%)
e 3049
 
10.2%
o 2896
 
9.7%
t 2867
 
9.6%
r 2536
 
8.5%
k 1767
 
5.9%
n 1761
 
5.9%
N 1639
 
5.5%
i 1597
 
5.3%
1500
 
5.0%
w 1465
 
4.9%
Other values (31) 8843
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22675
75.8%
Uppercase Letter 5519
 
18.4%
Space Separator 1500
 
5.0%
Dash Punctuation 162
 
0.5%
Connector Punctuation 64
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3049
13.4%
o 2896
12.8%
t 2867
12.6%
r 2536
11.2%
k 1767
7.8%
n 1761
7.8%
i 1597
7.0%
w 1465
6.5%
a 1401
6.2%
g 1001
 
4.4%
Other values (9) 2335
10.3%
Uppercase Letter
ValueCountFrequency (%)
N 1639
29.7%
C 1095
19.8%
P 981
17.8%
B 302
 
5.5%
E 285
 
5.2%
V 196
 
3.6%
T 167
 
3.0%
L 143
 
2.6%
R 128
 
2.3%
A 124
 
2.2%
Other values (9) 459
 
8.3%
Space Separator
ValueCountFrequency (%)
1500
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 162
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28194
94.2%
Common 1726
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3049
10.8%
o 2896
10.3%
t 2867
 
10.2%
r 2536
 
9.0%
k 1767
 
6.3%
n 1761
 
6.2%
N 1639
 
5.8%
i 1597
 
5.7%
w 1465
 
5.2%
a 1401
 
5.0%
Other values (28) 7216
25.6%
Common
ValueCountFrequency (%)
1500
86.9%
- 162
 
9.4%
_ 64
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3049
 
10.2%
o 2896
 
9.7%
t 2867
 
9.6%
r 2536
 
8.5%
k 1767
 
5.9%
n 1761
 
5.9%
N 1639
 
5.5%
i 1597
 
5.3%
1500
 
5.0%
w 1465
 
4.9%
Other values (31) 8843
29.6%

Latitude
Real number (ℝ)

Distinct1874
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.465745
Minimum45.562567
Maximum48.995255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-10-12T00:41:37.271018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum45.562567
5-th percentile46.053085
Q147.317812
median47.6146
Q347.661538
95-th percentile48.450226
Maximum48.995255
Range3.432688
Interquartile range (IQR)0.34372575

Descriptive statistics

Standard deviation0.59883197
Coefficient of variation (CV)0.012616087
Kurtosis2.4323527
Mean47.465745
Median Absolute Deviation (MAD)0.11255496
Skewness-1.11402
Sum92083.545
Variance0.35859973
MonotonicityNot monotonic
2023-10-12T00:41:37.360358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.613391 4
 
0.2%
47.616069 4
 
0.2%
47.61751 3
 
0.2%
46.97114 3
 
0.2%
47.228591 3
 
0.2%
47.613392 3
 
0.2%
47.616076 3
 
0.2%
47.613343 3
 
0.2%
47.616684 3
 
0.2%
47.5806355 2
 
0.1%
Other values (1864) 1909
98.4%
ValueCountFrequency (%)
45.562567 1
0.1%
45.562604 1
0.1%
45.562907 1
0.1%
45.563002 1
0.1%
45.56304 2
0.1%
45.578726 1
0.1%
45.6040109 1
0.1%
45.606403 1
0.1%
45.609302 1
0.1%
45.615549 1
0.1%
ValueCountFrequency (%)
48.995255 1
0.1%
48.990486 1
0.1%
48.989751 1
0.1%
48.987053 1
0.1%
48.942575 1
0.1%
48.938283 1
0.1%
48.937845 1
0.1%
48.92653 1
0.1%
48.89416 1
0.1%
48.862062 1
0.1%

Longitude
Real number (ℝ)

Distinct1891
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-121.88139
Minimum-124.66292
Maximum-117.0435
Zeros0
Zeros (%)0.0%
Negative1940
Negative (%)100.0%
Memory size15.3 KiB
2023-10-12T00:41:37.454733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-124.66292
5-th percentile-122.9096
Q1-122.37439
median-122.30086
Q3-122.17596
95-th percentile-117.54051
Maximum-117.0435
Range7.619428
Interquartile range (IQR)0.19843075

Descriptive statistics

Standard deviation1.3768619
Coefficient of variation (CV)-0.011296736
Kurtosis4.5748539
Mean-121.88139
Median Absolute Deviation (MAD)0.121977
Skewness2.2717993
Sum-236449.9
Variance1.8957486
MonotonicityNot monotonic
2023-10-12T00:41:37.545789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.346886 2
 
0.1%
-120.0199 2
 
0.1%
-122.22728 2
 
0.1%
-122.3594244 2
 
0.1%
-122.19644 2
 
0.1%
-122.21277 2
 
0.1%
-122.196441 2
 
0.1%
-122.206519 2
 
0.1%
-122.331009 2
 
0.1%
-122.298719 2
 
0.1%
Other values (1881) 1920
99.0%
ValueCountFrequency (%)
-124.662924 1
0.1%
-124.6600293 1
0.1%
-124.6593683 1
0.1%
-124.5444948 1
0.1%
-124.544249 1
0.1%
-124.3944043 1
0.1%
-124.385236 1
0.1%
-124.3715566 1
0.1%
-124.217215 1
0.1%
-124.216904 1
0.1%
ValueCountFrequency (%)
-117.043496 1
0.1%
-117.04865 1
0.1%
-117.098183 1
0.1%
-117.098508 1
0.1%
-117.098583 2
0.1%
-117.100062 1
0.1%
-117.102884 1
0.1%
-117.103246 1
0.1%
-117.110447 1
0.1%
-117.112915 1
0.1%

Date Last Confirmed
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct26
Distinct (%)1.3%
Missing2
Missing (%)0.1%
Memory size15.3 KiB
2023-09-29
1452 
2023-09-28
147 
2022-10-06
 
112
2023-07-01
 
46
2022-08-10
 
27
Other values (21)
154 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters19380
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st row2023-01-10
2nd row2023-08-10
3rd row2023-06-12
4th row2023-04-06
5th row2023-09-29

Common Values

ValueCountFrequency (%)
2023-09-29 1452
74.8%
2023-09-28 147
 
7.6%
2022-10-06 112
 
5.8%
2023-07-01 46
 
2.4%
2022-08-10 27
 
1.4%
2023-06-12 19
 
1.0%
2023-09-14 18
 
0.9%
2021-12-09 18
 
0.9%
2023-05-30 13
 
0.7%
2023-01-10 12
 
0.6%
Other values (16) 74
 
3.8%

Length

2023-10-12T00:41:37.631143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-09-29 1452
74.9%
2023-09-28 147
 
7.6%
2022-10-06 112
 
5.8%
2023-07-01 46
 
2.4%
2022-08-10 27
 
1.4%
2023-06-12 19
 
1.0%
2023-09-14 18
 
0.9%
2021-12-09 18
 
0.9%
2023-05-30 13
 
0.7%
2023-01-10 12
 
0.6%
Other values (16) 74
 
3.8%

Most occurring characters

ValueCountFrequency (%)
2 5716
29.5%
0 4124
21.3%
- 3876
20.0%
9 3100
16.0%
3 1745
 
9.0%
1 379
 
2.0%
8 185
 
1.0%
6 149
 
0.8%
7 57
 
0.3%
4 36
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15504
80.0%
Dash Punctuation 3876
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5716
36.9%
0 4124
26.6%
9 3100
20.0%
3 1745
 
11.3%
1 379
 
2.4%
8 185
 
1.2%
6 149
 
1.0%
7 57
 
0.4%
4 36
 
0.2%
5 13
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3876
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19380
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5716
29.5%
0 4124
21.3%
- 3876
20.0%
9 3100
16.0%
3 1745
 
9.0%
1 379
 
2.0%
8 185
 
1.0%
6 149
 
0.8%
7 57
 
0.3%
4 36
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5716
29.5%
0 4124
21.3%
- 3876
20.0%
9 3100
16.0%
3 1745
 
9.0%
1 379
 
2.0%
8 185
 
1.0%
6 149
 
0.8%
7 57
 
0.3%
4 36
 
0.2%

ID
Real number (ℝ)

UNIQUE 

Distinct1940
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189617.41
Minimum33351
Maximum311816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2023-10-12T00:41:37.708050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum33351
5-th percentile68935.3
Q1150111.25
median193611.5
Q3235327.25
95-th percentile309375.8
Maximum311816
Range278465
Interquartile range (IQR)85216

Descriptive statistics

Standard deviation66357.996
Coefficient of variation (CV)0.3499573
Kurtosis-0.40172707
Mean189617.41
Median Absolute Deviation (MAD)41743.5
Skewness-0.12025954
Sum3.6785777 × 108
Variance4.4033837 × 109
MonotonicityStrictly increasing
2023-10-12T00:41:37.792074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33351 1
 
0.1%
214131 1
 
0.1%
219848 1
 
0.1%
219789 1
 
0.1%
219788 1
 
0.1%
219758 1
 
0.1%
218938 1
 
0.1%
218668 1
 
0.1%
218510 1
 
0.1%
218507 1
 
0.1%
Other values (1930) 1930
99.5%
ValueCountFrequency (%)
33351 1
0.1%
33717 1
0.1%
35620 1
0.1%
35621 1
0.1%
37181 1
0.1%
38082 1
0.1%
38952 1
0.1%
38959 1
0.1%
38966 1
0.1%
39778 1
0.1%
ValueCountFrequency (%)
311816 1
0.1%
311815 1
0.1%
311814 1
0.1%
311707 1
0.1%
311665 1
0.1%
311654 1
0.1%
311650 1
0.1%
311642 1
0.1%
311588 1
0.1%
311562 1
0.1%
Distinct842
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
Minimum2021-03-11 23:22:17+00:00
Maximum2023-09-29 01:10:29+00:00
2023-10-12T00:41:37.880505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:37.974407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct877
Distinct (%)45.2%
Missing1
Missing (%)0.1%
Memory size15.3 KiB
Minimum2008-02-15 00:00:00
Maximum2023-09-28 00:00:00
2023-10-12T00:41:38.073213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:38.170434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

EV Connector Types
Categorical

IMBALANCE 

Distinct13
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
J1772
1557 
CHADEMO J1772COMBO
 
126
TESLA
 
112
J1772 TESLA
 
51
CHADEMO J1772 J1772COMBO
 
41
Other values (8)
 
53

Length

Max length24
Median length5
Mean length6.5716495
Min length5

Characters and Unicode

Total characters12749
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowJ1772
2nd rowNEMA520
3rd rowJ1772 NEMA520
4th rowJ1772
5th rowJ1772

Common Values

ValueCountFrequency (%)
J1772 1557
80.3%
CHADEMO J1772COMBO 126
 
6.5%
TESLA 112
 
5.8%
J1772 TESLA 51
 
2.6%
CHADEMO J1772 J1772COMBO 41
 
2.1%
J1772COMBO 24
 
1.2%
CHADEMO J1772 18
 
0.9%
NEMA520 3
 
0.2%
J1772 NEMA1450 2
 
0.1%
NEMA1450 2
 
0.1%
Other values (3) 4
 
0.2%

Length

2023-10-12T00:41:38.257778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
j1772 1672
75.2%
j1772combo 193
 
8.7%
chademo 186
 
8.4%
tesla 163
 
7.3%
nema520 4
 
0.2%
nema1450 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
7 3730
29.3%
1 1869
14.7%
2 1869
14.7%
J 1865
14.6%
O 572
 
4.5%
M 387
 
3.0%
C 379
 
3.0%
A 357
 
2.8%
E 357
 
2.8%
282
 
2.2%
Other values (10) 1082
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7488
58.7%
Uppercase Letter 4979
39.1%
Space Separator 282
 
2.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J 1865
37.5%
O 572
 
11.5%
M 387
 
7.8%
C 379
 
7.6%
A 357
 
7.2%
E 357
 
7.2%
B 193
 
3.9%
H 186
 
3.7%
D 186
 
3.7%
T 163
 
3.3%
Other values (3) 334
 
6.7%
Decimal Number
ValueCountFrequency (%)
7 3730
49.8%
1 1869
25.0%
2 1869
25.0%
5 8
 
0.1%
0 8
 
0.1%
4 4
 
0.1%
Space Separator
ValueCountFrequency (%)
282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7770
60.9%
Latin 4979
39.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 1865
37.5%
O 572
 
11.5%
M 387
 
7.8%
C 379
 
7.6%
A 357
 
7.2%
E 357
 
7.2%
B 193
 
3.9%
H 186
 
3.7%
D 186
 
3.7%
T 163
 
3.3%
Other values (3) 334
 
6.7%
Common
ValueCountFrequency (%)
7 3730
48.0%
1 1869
24.1%
2 1869
24.1%
282
 
3.6%
5 8
 
0.1%
0 8
 
0.1%
4 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 3730
29.3%
1 1869
14.7%
2 1869
14.7%
J 1865
14.6%
O 572
 
4.5%
M 387
 
3.0%
C 379
 
3.0%
A 357
 
2.8%
E 357
 
2.8%
282
 
2.2%
Other values (10) 1082
 
8.5%

EV Pricing
Text

MISSING 

Distinct60
Distinct (%)12.5%
Missing1460
Missing (%)75.3%
Memory size15.3 KiB
2023-10-12T00:41:38.411586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length105
Median length4
Mean length13.095833
Min length4

Characters and Unicode

Total characters6286
Distinct characters52
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)7.9%

Sample

1st rowFree; 3 hour maximum charging session
2nd rowFree
3rd rowFree
4th rowFree
5th rowFree
ValueCountFrequency (%)
free 343
31.6%
fee 129
 
11.9%
parking 107
 
9.8%
variable 46
 
4.2%
per 36
 
3.3%
for 31
 
2.9%
session 31
 
2.9%
monthly 26
 
2.4%
19.99 26
 
2.4%
or 26
 
2.4%
Other values (71) 286
26.3%
2023-10-12T00:41:38.667115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1046
16.6%
r 688
 
10.9%
607
 
9.7%
F 479
 
7.6%
0 327
 
5.2%
a 242
 
3.8%
$ 232
 
3.7%
n 217
 
3.5%
. 214
 
3.4%
i 190
 
3.0%
Other values (42) 2044
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3368
53.6%
Uppercase Letter 928
 
14.8%
Decimal Number 708
 
11.3%
Space Separator 607
 
9.7%
Other Punctuation 394
 
6.3%
Currency Symbol 232
 
3.7%
Dash Punctuation 48
 
0.8%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1046
31.1%
r 688
20.4%
a 242
 
7.2%
n 217
 
6.4%
i 190
 
5.6%
k 134
 
4.0%
g 133
 
3.9%
o 122
 
3.6%
l 116
 
3.4%
s 114
 
3.4%
Other values (13) 366
 
10.9%
Uppercase Letter
ValueCountFrequency (%)
F 479
51.6%
P 104
 
11.2%
H 102
 
11.0%
E 94
 
10.1%
V 46
 
5.0%
R 36
 
3.9%
W 28
 
3.0%
L 14
 
1.5%
D 12
 
1.3%
C 12
 
1.3%
Decimal Number
ValueCountFrequency (%)
0 327
46.2%
1 94
 
13.3%
5 80
 
11.3%
9 78
 
11.0%
2 75
 
10.6%
4 19
 
2.7%
7 18
 
2.5%
3 15
 
2.1%
8 2
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 214
54.3%
/ 124
31.5%
: 26
 
6.6%
, 22
 
5.6%
; 8
 
2.0%
Space Separator
ValueCountFrequency (%)
607
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 232
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4296
68.3%
Common 1990
31.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1046
24.3%
r 688
16.0%
F 479
11.1%
a 242
 
5.6%
n 217
 
5.1%
i 190
 
4.4%
k 134
 
3.1%
g 133
 
3.1%
o 122
 
2.8%
l 116
 
2.7%
Other values (24) 929
21.6%
Common
ValueCountFrequency (%)
607
30.5%
0 327
16.4%
$ 232
 
11.7%
. 214
 
10.8%
/ 124
 
6.2%
1 94
 
4.7%
5 80
 
4.0%
9 78
 
3.9%
2 75
 
3.8%
- 48
 
2.4%
Other values (8) 111
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1046
16.6%
r 688
 
10.9%
607
 
9.7%
F 479
 
7.6%
0 327
 
5.2%
a 242
 
3.8%
$ 232
 
3.7%
n 217
 
3.5%
. 214
 
3.4%
i 190
 
3.0%
Other values (42) 2044
32.5%

Interactions

2023-10-12T00:41:32.048151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:29.842594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.268427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.696450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.116705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.582446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:32.112248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:29.927991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.330943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.755805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.187194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.647669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:32.182517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:29.992416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.401242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.823009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.259942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.724685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:32.254020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.065352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.468493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.896338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.336284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.799931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:32.332838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.137187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.546995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.971198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.420367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.885681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:32.418770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.203765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:30.626482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.047422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.504566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-12T00:41:31.970443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-12T00:41:38.759181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EV Level1 EVSE NumEV Level2 EVSE NumEV DC Fast CountLatitudeLongitudeIDGroups With Access CodeEV NetworkDate Last ConfirmedEV Connector Types
EV Level1 EVSE Num1.000-1.000NaN-0.464-0.439-0.0240.0001.0000.0000.000
EV Level2 EVSE Num-1.0001.0000.0220.018-0.0090.0620.0650.2020.1700.000
EV DC Fast CountNaN0.0221.0000.054-0.048-0.0590.1000.4170.3070.376
Latitude-0.4640.0180.0541.0000.062-0.0260.0730.1460.1640.102
Longitude-0.439-0.009-0.0480.0621.000-0.0620.1200.2590.2360.150
ID-0.0240.062-0.059-0.026-0.0621.0000.2020.3290.3390.244
Groups With Access Code0.0000.0650.1000.0730.1200.2021.0000.2830.5890.187
EV Network1.0000.2020.4170.1460.2590.3290.2831.0000.5520.468
Date Last Confirmed0.0000.1700.3070.1640.2360.3390.5890.5521.0000.444
EV Connector Types0.0000.0000.3760.1020.1500.2440.1870.4680.4441.000

Missing values

2023-10-12T00:41:32.537877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-12T00:41:32.732041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-12T00:41:32.877018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Station NameStreet AddressCityStateZIPGroups With Access CodeAccess Days TimeEV Level1 EVSE NumEV Level2 EVSE NumEV DC Fast CountEV NetworkLatitudeLongitudeDate Last ConfirmedIDUpdated AtOpen DateEV Connector TypesEV Pricing
0City of Lacey - City Hall Parking420 College StLaceyWA98503Public24 hours dailyNaN4.0NaNNon-Networked47.044011-122.8224042023-01-10333512023-02-14 15:54:11 UTC2018-01-15J1772Free; 3 hour maximum charging session
1Seattle-Tacoma International Airport - General Parking17801 Pacific Hwy SSeattleWA98188Public24 hours daily; pay lot; Drivers must bring their own J1772 cordset for Level 1 charging121.0NaNNaNNon-Networked47.443377-122.2962292023-08-10337172023-08-10 16:58:49 UTC2010-03-01NEMA520Free
2Avista Corp1411 E Mission AveSpokaneWA99252Public24 hours daily; Drivers must bring their own J1772 cordset for Level 1 charging1.02.0NaNNon-Networked47.673347-117.3889332023-06-12356202023-06-12 16:56:16 UTC2010-04-15J1772 NEMA520Free
3Steam Plant Parking130 S Post StSpokaneWA99201Public24 hours dailyNaN6.0NaNNon-Networked47.655792-117.4236642023-04-06356212023-04-06 17:17:01 UTC2010-04-15J1772Free
4BELLEVUE BELLEVUE CH 1450 110th Ave NEBellevueWA98004Public24 hours dailyNaN2.0NaNChargePoint Network47.614744-122.1931622023-09-29371812023-09-29 00:18:15 UTC2010-12-15J1772NaN
5KING COUNTY DES ISSAQUAH P&R 21755 Highlands Dr NEIssaquahWA98027Public24 hours dailyNaN2.0NaNChargePoint Network47.545324-122.0195002023-09-29380822023-09-29 00:32:33 UTC2008-02-15J1772NaN
6UWB CASCADIA CC NORTH LEVEL 3 N18500 Campus Way NEBothellWA98011Public24 hours dailyNaN2.0NaNChargePoint Network47.761878-122.1919152023-09-29389522023-09-29 00:14:58 UTC2011-03-15J1772NaN
7CITY OF REDMOND CITY HALL DUAL15670 NE 85th StRedmondWA98052Public24 hours dailyNaN2.0NaNChargePoint Network47.679224-122.1299502023-09-29389592023-09-29 00:10:28 UTC2011-03-15J1772NaN
8CITY OF REDMOND MOC #1 PUBLIC18080 NE 76th StRedmondWA98052Public24 hours dailyNaN2.0NaNChargePoint Network47.672359-122.0990342023-09-29389662023-09-29 00:10:28 UTC2011-03-15J1772NaN
9Bellingham Nissan1516 Iowa StBellinghamWA98229Public - Call aheadDealership business hoursNaN2.01.0Non-Networked48.755607-122.4541322020-06-09397782021-03-11 23:22:17 UTC2011-03-15CHADEMO J1772Free
Station NameStreet AddressCityStateZIPGroups With Access CodeAccess Days TimeEV Level1 EVSE NumEV Level2 EVSE NumEV DC Fast CountEV NetworkLatitudeLongitudeDate Last ConfirmedIDUpdated AtOpen DateEV Connector TypesEV Pricing
1930CP RETAIL CUSTPK7815 S Tacoma WayTacomaWA98409Public24 hours dailyNaN2.0NaNChargePoint Network47.185951-122.4826512023-09-293115622023-09-29 00:52:54 UTC2023-09-26J1772NaN
1931FOOTHILLS TOY CT4K1881 Bouslog RdBurlingtonWA98233Public24 hours dailyNaN2.0NaNChargePoint Network48.449451-122.3463132023-09-293115882023-09-29 00:54:25 UTC2023-09-26J1772NaN
19321700 Seventh1700 7th Avenue, Suite 1800SeattleWA98101PublicMon 6:00am - 9:00pm; Tue 6:00am - 9:00pm; Wed 6:00am - 9:00pm; Thu 6:00am - 9:00pm; Fri 6:00am - 9:00pm; Sat 9:00am - 9:00pm; Sun 9:00am - 9:00pmNaN8.0NaNBlink Network47.614019-122.3352732023-09-293116422023-09-29 00:00:42 UTC2023-06-16J1772NaN
1933Skagit Valley Casino5984 Darrk LnBowWA98232Public24 hours dailyNaN4.0NaNSHELL_RECHARGE48.559758-122.3471342023-09-293116502023-09-29 00:59:14 UTC2023-09-27J1772NaN
1934Sun Mountain Lodge604 Patterson Lake RdWinthropWA98862Public24 hours dailyNaN2.0NaNFLO48.476517-120.2578602023-09-293116542023-09-29 01:10:29 UTC2023-09-27J1772NaN
1935Airmark Apartments/Hotel Interurban229 Andover Park EastTukwilaWA98188Public24 hours dailyNaN2.0NaNBlink Network47.457052-122.2506782023-09-283116652023-09-28 23:58:50 UTC2023-08-02J1772NaN
1936MCREF III Lacey Apartments, LLC5333 15th Avenue NortheastLaceyWA98516Public24 hours dailyNaN1.0NaNBlink Network47.055925-122.8119052023-09-293117072023-09-29 00:04:48 UTC2023-09-18J1772NaN
1937Moran Prairie Library6004 S. Regal StSpokaneWA99223PublicNaNNaNNaN2.0EV Connect47.599475-117.3695432023-09-293118142023-09-29 01:05:05 UTC2023-09-28J1772COMBONaN
1938West Valley School8900 E. Buckeye Ave.8900 E. Buckeye Ave.WA99212PublicNaNNaNNaN2.0EV Connect47.682290-117.2853392023-09-293118152023-09-29 01:05:05 UTC2023-09-28J1772COMBONaN
1939North Spokane Library44 E. Hawthorne Rd.SpokaneWA99218PublicNaNNaNNaN2.0EV Connect47.750782-117.4090022023-09-293118162023-09-29 01:05:05 UTC2023-09-28J1772COMBONaN